code: def test_bench_equality():
      """[Model Selection] Test benchmark correspondence with eval."""
  
      with open(os.devnull, "w") as f, redirect_stderr(f):
          evl = Evaluator(mape_scorer, cv=5)
          evl.fit(
              X,
              y,
              estimators={"pr": [OLS()], "no": [OLS()]},
              param_dicts={},
              preprocessing={"pr": [Scale()], "no": []},
          )
  
          out = benchmark(
              X,
              y,
              mape_scorer,
              5,
              {"pr": [OLS()], "no": [OLS()]},
              {"pr": [Scale()], "no": []},
              None,
          )
  
      np.testing.assert_approx_equal(
          out["test_score-m"]["no.ols"],
          evl.results["test_score-m"]["no.ols"],
      )
  
file_path: mlens/mlens/model_selection/tests/test_model_selection.py
imports: import os
  import numpy
  from mlens.model_selection import Evaluator, benchmark
  from mlens.metrics import mape, make_scorer
  from mlens.utils.exceptions import FitFailedWarning
  from mlens.utils.dummy import OLS, Scale
  from mlens.testing import Data
  from scipy.stats import randint
label_desc: Use image node_id 10 for calling a global function with example usage: test_bench_equality() without return types